np.sign() for sign detection in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.sign() on mixed values

What is the output of the following code?

import numpy as np
arr = np.array([-3, 0, 4, -1, 2])
signs = np.sign(arr)
print(signs)

NumPy

import numpy as np
arr = np.array([-3, 0, 4, -1, 2])
signs = np.sign(arr)
print(signs)

A [-1 0 1 -1 1]

B [1 0 1 1 1]

C [-1 1 1 -1 1]

D [0 0 0 0 0]

💡 Hint

np.sign returns -1 for negative, 0 for zero, and 1 for positive values.

np.sign returns an array where each element is -1 if the original element is negative, 0 if zero, and 1 if positive. So for [-3, 0, 4, -1, 2], the output is [-1, 0, 1, -1, 1].

❓ data_output

intermediate

Count of positive, negative, and zero values using np.sign()

Given the array below, what is the count of positive, negative, and zero values using np.sign()?

import numpy as np
arr = np.array([5, -2, 0, 7, -8, 0, 3])
signs = np.sign(arr)
counts = {"positive": np.sum(signs == 1), "negative": np.sum(signs == -1), "zero": np.sum(signs == 0)}
print(counts)

NumPy

import numpy as np
arr = np.array([5, -2, 0, 7, -8, 0, 3])
signs = np.sign(arr)
counts = {"positive": np.sum(signs == 1), "negative": np.sum(signs == -1), "zero": np.sum(signs == 0)}
print(counts)

A {"positive": 3, "negative": 2, "zero": 2}

B {"positive": 4, "negative": 2, "zero": 1}

C {"positive": 3, "negative": 3, "zero": 1}

D {"positive": 2, "negative": 2, "zero": 3}

💡 Hint

Count how many elements are 1, -1, and 0 in the signs array.

The array has 3 positive numbers (5,7,3), 2 negative numbers (-2,-8), and 2 zeros (0,0). np.sign returns 1, -1, and 0 respectively, so counts match accordingly.

🔧 Debug

advanced

Identify the error in np.sign usage

What error will this code raise?

import numpy as np
arr = np.array([1, -1, 0])
signs = np.sign(arr, out=[0,0,0])
print(signs)

NumPy

import numpy as np
arr = np.array([1, -1, 0])
signs = np.sign(arr, out=[0,0,0])
print(signs)

A AttributeError: 'list' object has no attribute 'dtype'

B ValueError: shape mismatch

C TypeError: 'out' must be a ndarray

D No error, prints [1 -1 0]

💡 Hint

The 'out' parameter expects a numpy array, not a list.

The 'out' parameter in np.sign must be a numpy ndarray to store the output. Passing a Python list causes a TypeError.

🚀 Application

advanced

Using np.sign to flip signs conditionally

You want to flip the sign of all negative numbers in an array but keep positive and zero values unchanged. Which code snippet achieves this using np.sign() and numpy operations?

A

arr = np.array([-3, 0, 4, -1, 2])
result = arr * np.sign(arr)

B

arr = np.array([-3, 0, 4, -1, 2])
result = arr * (np.sign(arr) == 1) + arr * (np.sign(arr) == 0)

C

arr = np.array([-3, 0, 4, -1, 2])
result = np.abs(arr)

D

arr = np.array([-3, 0, 4, -1, 2])
result = arr * (np.sign(arr) == -1) * -1 + arr * (np.sign(arr) != -1)

💡 Hint

Use np.sign to detect negatives and multiply by -1 to flip them.

Option D flips negative values by multiplying them by -1 and keeps others unchanged. Option D converts all to positive, which is not the same as flipping only negatives. Options B and D do not flip negatives correctly.

🧠 Conceptual

expert

Behavior of np.sign with floating point edge cases

What is the output of np.sign() when applied to the array containing special floating point values?

import numpy as np
arr = np.array([-0.0, 0.0, np.nan, np.inf, -np.inf])
signs = np.sign(arr)
print(signs)

NumPy

import numpy as np
arr = np.array([-0.0, 0.0, np.nan, np.inf, -np.inf])
signs = np.sign(arr)
print(signs)

A [-0. 0. nan 1. -1.]

B [0. 0. nan 1. -1.]

C [-1. 0. nan 1. -1.]

D [0. 0. 0. 1. -1.]

💡 Hint

np.sign returns 0 for both +0.0 and -0.0, and nan for np.nan.

np.sign returns 0 for both +0.0 and -0.0 (no negative zero in output), nan for np.nan, 1 for positive infinity, and -1 for negative infinity.

Practice

1. What does the np.sign() function return when applied to a negative number?

easy

A. -1

B. 0

C. 1

D. The original number

Solution

1. Step 1: Understand np.sign() behavior

   The function returns -1 for negative numbers, 0 for zero, and 1 for positive numbers.

2. Step 2: Apply to a negative number

   Since the input is negative, np.sign() returns -1.

3. Final Answer:

   -1 -> Option A

4. Quick Check:

   Negative number sign = -1 [OK]

Hint: Negative input always gives -1 from np.sign() [OK]

Common Mistakes:

• Confusing negative with zero
• Expecting original number as output
• Thinking it returns boolean

2. Which of the following is the correct syntax to get the sign of each element in a numpy array arr?

easy

A. np.sign(arr)

B. arr.sign()

C. sign(arr)

D. np.sign_of(arr)

Solution

1. Step 1: Recall numpy function usage

   Functions in numpy are called with the syntax np.function_name(arguments).

2. Step 2: Identify correct function call

   The correct function to get sign is np.sign(), so np.sign(arr) is correct.

3. Final Answer:

   np.sign(arr) -> Option A

4. Quick Check:

   Correct numpy function call = np.sign(arr) [OK]

Hint: Use np.sign(array) to get signs of all elements [OK]

Common Mistakes:

• Using method on array like arr.sign()
• Calling sign() without np prefix
• Using non-existent np.sign_of()

3. What is the output of the following code?

import numpy as np
arr = np.array([-3, 0, 4])
sign_arr = np.sign(arr)
print(sign_arr)

medium

A. [-3 0 4]

B. [3 0 4]

C. [0 0 0]

D. [-1 0 1]

Solution

1. Step 1: Understand input array values

   The array has values -3 (negative), 0 (zero), and 4 (positive).

2. Step 2: Apply np.sign() to each element

   np.sign(-3) = -1, np.sign(0) = 0, np.sign(4) = 1, so the output array is [-1, 0, 1].

3. Final Answer:

   [-1 0 1] -> Option D

4. Quick Check:

   Signs of [-3,0,4] = [-1,0,1] [OK]

Hint: np.sign() maps negative to -1, zero to 0, positive to 1 [OK]

Common Mistakes:

• Expecting original values
• Confusing zero with positive
• Outputting boolean instead of sign

4. The following code throws an error. What is the mistake?

import numpy as np
arr = [-1, 2, 0]
signs = np.sign arr
print(signs)

medium

A. print() syntax error

B. Using list instead of numpy array

C. Missing parentheses in function call

D. np.sign does not exist

Solution

1. Step 1: Check function call syntax

   The code uses np.sign arr without parentheses, which is invalid syntax in Python.

2. Step 2: Correct the syntax

   It should be np.sign(arr) with parentheses to call the function properly.

3. Final Answer:

   Missing parentheses in function call -> Option C

4. Quick Check:

   Function calls need parentheses [OK]

Hint: Always use parentheses when calling functions [OK]

Common Mistakes:

• Omitting parentheses
• Thinking lists cause error here
• Assuming np.sign is undefined

5. You have a numpy array data = np.array([-5, 0, 3, -2, 7]). How can you create a new array that replaces all negative values with 0, using np.sign()?

hard

A. data * np.sign(data)

B. data * (np.sign(data) + 1) / 2

C. np.sign(data) * 2

D. data + np.sign(data)

Solution

1. Step 1: Understand np.sign() output

   np.sign(data) gives -1 for negatives, 0 for zero, 1 for positives.

2. Step 2: Transform sign to mask for positives and zero

   Adding 1 to sign gives 0 for -1, 1 for 0, 2 for 1. Dividing by 2 maps negatives to 0, zero to 0.5, positives to 1.

3. Step 3: Multiply original data by this mask

   Multiplying data by this mask sets negative values to 0, keeps zero and positive values unchanged (zero times 0.5 is 0).

4. Final Answer:

   data * (np.sign(data) + 1) / 2 -> Option B

5. Quick Check:

   Mask negatives to zero using (sign+1)/2 [OK]

Hint: Use (sign+1)/2 as mask to zero negatives [OK]

Common Mistakes:

• Using sign directly multiplies negatives
• Adding sign to data changes values wrongly
• Confusing mask calculation